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Ramnath GS, Harikrishnan R, Muyeen SM, Kotecha K. Household electricity consumption prediction using database combinations, ensemble and hybrid modeling techniques. Sci Rep 2024; 14:22891. [PMID: 39358367 PMCID: PMC11447179 DOI: 10.1038/s41598-024-57550-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 03/19/2024] [Indexed: 10/04/2024] Open
Abstract
Household electricity consumption (HEC) is changing over time, depends on multiple factors, and leads to effects on the prediction accuracy of the model. The objective of this work is to propose a novel methodology for improving HEC prediction accuracy. This study uses two original datasets, namely questionnaire survey (QS) and monthly consumption (MC), which contain data from 225 consumers from Maharashtra, India. The original datasets are combined to create three additional datasets, namely QS + MC, QS equation (QsEq) + next month's consumptions, and QsEq + MC. Furthermore, the HEC prediction accuracy is boosted by applying different approaches, like correlation methods, feature engineering techniques, data quality assessment, heterogeneous ensemble prediction (HEP), and the hybrid model. Five HEP models are created using dataset combinations and machine learning algorithms. Based on the MC dataset, the random forest provides the best prediction of RMSE (36.18 kWh), MAE (25.73 kWh), and R2 (0.76). Similarly, QsEq + MC dataset adaptive boosting provides a better prediction of RMSE (36.77 kWh), MAE (26.18 kWh), and R2 (0.76). This prediction accuracy is further increased using the proposed hybrid model to RMSE (22.02 kWh), MAE (13.04 kWh), and R2 (0.92). This research work benefits researchers, policymakers, and utility companies in obtaining accurate prediction models and understanding HEC.
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Affiliation(s)
- Gaikwad Sachin Ramnath
- Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed) University, Pune, India
| | - R Harikrishnan
- Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed) University, Pune, India.
| | - S M Muyeen
- Department of Electrical Engineering, Qatar University, 2713, Doha, Qatar.
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
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Liu J, Zhou Z, Kong S, Ma Z. Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs. Front Oncol 2022; 12:956705. [PMID: 35936743 PMCID: PMC9353770 DOI: 10.3389/fonc.2022.956705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/28/2022] [Indexed: 11/19/2022] Open
Abstract
The optimization of drug properties in the process of cancer drug development is very important to save research and development time and cost. In order to make the anti-breast cancer drug candidates with good biological activity, this paper collected 1974 compounds, firstly, the top 20 molecular descriptors that have the most influence on biological activity were screened by using XGBoost-based data feature selection; secondly, on this basis, take pIC50 values as feature data and use a variety of machine learning algorithms to compare, soas to select a most suitable algorithm to predict the IC50 and pIC50 values. It is preliminarily found that the effects of Random Forest, XGBoost and Gradient-enhanced algorithms are good and have little difference, and the Support vector machine is the worst. Then, using the Semi-automatic parameter adjustment method to adjust the parameters of Random Forest, XGBoost and Gradient-enhanced algorithms to find the optimal parameters. It is found that the Random Forest algorithm has high accuracy and excellent anti over fitting, and the algorithm is stable. Its prediction accuracy is 0.745. Finally, the accuracy of the results is verified by training the model with the preliminarily selected data, which provides an innovative solution for the optimization of the properties of anti- breast cancer drugs, and can provide better support for the early research and development of anti-breast cancer drugs.
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Affiliation(s)
- Jiajia Liu
- College of Science, North China University of Science and Technology, Tangshan, China
- Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, China
- The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, China
| | - Zhihui Zhou
- College of Science, North China University of Science and Technology, Tangshan, China
- The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, China
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, China
| | - Shanshan Kong
- College of Science, North China University of Science and Technology, Tangshan, China
- Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, China
- The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, China
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, China
- Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, China
- *Correspondence: Shanshan Kong,
| | - Zezhong Ma
- College of Science, North China University of Science and Technology, Tangshan, China
- Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, China
- Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, China
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